Gartner predicts that global IT spending will grow by 6.8% in 2024
Despite the wave of layoffs in the technology industry, the IT market continues to grow. According to Gartner's latest report, global IT spending is expected to reach US$5 trillion in 2024, with a growth rate of 6.8%, indicating that the IT industry still has strong development potential.
#Gartner’s latest report is a research and consulting study on IT spending that provides a unique perspective. They previously forecast IT spending growth of 8%, and although they have adjusted their forecasts, they still expect IT spending to grow more than double the previous year. This report covers spending in different areas such as IT services, hardware and software. As a leading market research and consulting company, Gartner's views are very valuable in understanding the development trends and market trends of the IT industry.
It is worth noting that, according to Gartner data, GenAI (generative artificial intelligence) will not significantly change the growth of IT spending in the short term.
According to Gartner Distinguished Vice President Analyst John-David Lovelock, although GenAI will bring significant changes, the impact on IT spending will not be as significant compared to technology trends such as the Internet of Things and blockchain. Significantly.
According to Lovelock, 2024 will see more focus on how to effectively utilize GenAI. The major share of IT spending will continue to be driven by traditional factors such as labor and profitability. However, these expenditures will be negatively impacted by the ongoing wave of “change fatigue,” where organizations feel overwhelmed by too much change. Therefore, organizations will need to take appropriate measures to alleviate this fatigue, such as planning change strategies more precisely, providing adequate training and support, etc.
According to a Gartner report, CIOs generally face the problem of change fatigue and are increasingly less receptive to long-term plans and new technology partners. They want to know with greater certainty the results of new initiatives and reduce risks.
Despite the challenge of change fatigue, IT services are expected to experience the largest growth of 8.7% by 2024, becoming the major component of IT spending, reaching $1.5 trillion. The key driver behind this unprecedented growth is increased investment in project optimization and organizational efficiency.
According to forecasts, after a sharp decline in IT spending in 2023, it is expected to achieve a healthy growth of 4.6% in 2024. Excessive growth in IT equipment spending during the epidemic has led to a collapse in equipment spending in 2023. However, as the economy gradually recovers, demand for IT equipment is expected to rebound, bringing new growth opportunities to the industry.
Spending on data center systems and software continued to grow, with growth rates of 7.5% and 8.7% respectively. According to Gartner's forecast, the communications services industry has the lowest growth rate, accounting for only 2.3% of total IT spending.
While IT has traditionally been a back-office function, it has now become a major revenue line. “More than a decade ago, consumer adoption of devices and communications services stagnated. Consumer spending levels were primarily driven by price changes and replacement cycles, leaving only room for incremental growth, so being overtaken by software and services was impossible. Avoided," Lovelock said.
Gartner’s report reveals market opportunities and challenges. Businesses that can invest wisely in IT services will be well-positioned to reap long-term returns from this rapidly growing field.
The above is the detailed content of Gartner predicts that global IT spending will grow by 6.8% in 2024. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics

This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP

Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application

To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text

Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it

Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing

Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A

Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S

According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year
